Partition: a surjective mapping approach for dimensionality reduction.

ABSTRACT:

Motivation

Large amounts of information generated by genomic technologies are accompanied by statistical and computational challenges due to redundancy, badly behaved data and noise. Dimensionality reduction (DR) methods have been developed to mitigate these challenges. However, many approaches are not scalable to large dimensions or result in excessive information loss.

Results

The proposed approach partitions data into subsets of related features and summarizes each into one and only one new feature, thus defining a surjective mapping. A constraint on information loss determines the size of the reduced dataset. Simulation studies demonstrate that when multiple related features are associated with a response, this approach can substantially increase the number of true assoc

SUBMITTER: Millstein J 

PROVIDER: S-EPMC8215926 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature